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Time for a change: Temporal perspectives and health goals
Personality and Individual Differences 109 (2017) 220–224
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Time for a change: Temporal perspectives and health goals Amy Knepple Carney ⁎, Julie Hicks Patrick Department of Psychology, West Virginia University, United States
a r t i c l e
i n f o
Article history: Received 19 September 2016 Received in revised form 23 December 2016 Accepted 9 January 2017 Available online xxxx Keywords: Temporal perspectives Health goals
a b s t r a c t Future time perspective influences the relations among age and health behaviors (Stahl & Patrick, 2012). However, past and present temporal perspectives may also play a role in the goals that people pursue. Whether age interacts with temporal perspectives to influence health goals has not been fully examined. Thus, the aim of the current study was to examine the associations among age, different temporal frames, and behavioral change goals related to health. We examined these questions using data provided by 253 adults, ages 18–87 years (M = 40.74, SD = 14.82). Results from mediational regression analyses showed that age and a full range of valenced temporal perspectives accounted for 12% of the variance in the number of health goals adults endorsed. Only positive present temporal perspective related directly to health goals. By using a broader measure of temporal frames simultaneously, we may have a more accurate view of temporal perspective in adulthood. © 2017 Elsevier Ltd. All rights reserved.
1. Introduction Temporal perspectives involve attitudes, thoughts, and affective tone regarding our personal past, present, and future. As such, temporal perspectives influence our current and future behaviors (Lennings, 2000). Although a multidimensional construct comprised of temporal frames (i.e., past, present, future), depth or length of extension (e.g., near past, far future), emotional valence (e.g., positive, neutral, negative), structuration or degree of continuity across temporal frames, and the vividness and realism of one's views (Kazakina, 2015; Nuttin & Lens, 1985), most researchers have considered only one or two of the dimensions of temporal perspectives, with the bulk of research focusing on future time perspectives (Boniwell & Zimbardo, 2004). The perception of future time as either expansive or limited influences most behaviors and psychological processes in adulthood (Carstensen, 2006). Goal selection is inherently a future-focused behavior that is influenced by our temporal perspective (Baltes & Baltes, 1990; Deci & Ryan, 2000). Relative to a narrow future horizon, individuals with an expansive future time perspective generally have more goals (Brothers, Chui, & Diehl, 2014), more motivation to work on goals (Lens, Paixao, Herrera, & Grobler, 2012), anticipate having energy to complete tasks (Stolarski, Matthews, Postek, Zimbardo, & Bitner, 2014), and report more frequent engagement in health-promoting behaviors (Gellert, Ziegelmann, Lippke, & Schwarzer, 2012; Henson, Carey, Carey, & Maisto, 2006; Stahl & Patrick, 2012). Although there is some literature to suggest that future goals are directly shaped by our present feelings and behaviors (Lewin, 1939; Nuttin & Lens, 1985), ⁎ Corresponding author. E-mail address: [email protected] (A. Knepple Carney).
http://dx.doi.org/10.1016/j.paid.2017.01.015 0191-8869/© 2017 Elsevier Ltd. All rights reserved.
whether past and the present influence goal selection remains relatively unexamined.
1.1. Time and health-related goals Much of the current literature on the role of temporal perspective and health behaviors focuses on discrete health behaviors among college students or among community-dwelling younger and middleaged adults. Kornadt and Rothermund (2014) discussed the implications of focusing on domain-general versus domain-specific behaviors, including the need to isolate underlying motivations for behavior change within domains. Of course, extending investigations beyond midlife is also an important endeavor, as differences in goals and in the motivations for pursuing those goals may vary across age, temporal perspective, or both (Rossi & Isaacowitz, 2006). Using data from a large group of undergraduates (N = 1568), Henson et al. (2006) found that students with an expansive future perspective were less likely to engage in high-risk health behaviors, such as substance abuse. Expansive future time perspective was also related to health-promotion behaviors, including more exercise, more frequent condom use, and among women, use of birth control. Similar results are found in non-college samples of adults, as well. Across several different nations and age groups, an expansive future time perspective predicts successful smoking cessation 8 years later (Hall, Fong, & Meng, 2014). Future orientation has been linked to health promotion behaviors in community-dwelling adults, as well. Among younger and middle-aged adults, Stahl and Patrick (2012) reported that age effects on physical activity were partially mediated by future perspective. Gellert et al. (2012) also reported associations with future
A. Knepple Carney, J.H. Patrick / Personality and Individual Differences 109 (2017) 220–224
perspective and physical activity goals to predict physical activity 6 months after baseline. Although there is evidence that future perspective influences both risky health behaviors and health promotion behaviors, few studies have simultaneously examined other temporal frames. An exception is a recent study reported by Griva, Tseferidi, and Anagnostopoulos (2015). Using data from more than 400 adults (ages 18–56 years), they reported a link between future orientation and physical exercise, but they also found roles for past and present orientations as related to body mass index (BMI). Thus, a growing literature supports the relations among various temporal frames and specific health behaviors, in isolation. Whether and in what ways past, present, and future frames influence risky health behaviors and health promotion behaviors among a wide age range of adults have not been examined. 1.2. Current study The current study focuses on goals to improve physical health because they are relatively under-studied in the area of temporal perspective and because they have important implications for communitybased health promotion efforts (Fisher et al., 2011). Based on the work by Rossi and Isaacowitz (2006) and Kornadt and Rothermund (2014), we anticipated that younger adults would have fewer health change goals relative to their more senior counterparts. However, there is little evidence to suggest whether age differences would be evident in the type of health goals endorsed. We also anticipated significant associations between age and temporal perspectives (Carstensen, Isaacowitz, & Charles, 1999; Spreng & Levine, 2006). The main focus, however, was to examine whether temporal frames governed the association between the number health goals across age groups. Although Fingerman and Perlmutter (2001) suggested that time might operate differently across age groups, research examining the effects of multiple temporal perspectives is rare. 2. Method 2.1. Power analysis A formal power analysis, implemented in G*Power (Erdfeld, Faul, & Buchner, 1996), suggested that data from 253 adults would provide sufficient power (power N 0.95) to detect medium-sized effects (f 2 = 0.15) in a 7-variable regression equation (p b 0.05). 2.2. Participants Participants were drawn from community-dwelling adults who responded to print and electronic ads to complete an online prescreen for a health coaching study. The prescreen consisted of a 25-page online survey. Participants completed the survey in about 38 min (SD = 31.4, range 25–60 min). Although the prescreen for the larger study included a variety of measures (e.g., physical and mental health, decision making, technology use, and social exchanges), only those related to the current study are discussed in this report. All participants were offered $5 for completing the survey. A total of 285 adults started the instrument, with 253 completing the temporal perspective and demographic items. Respondents consisted of 61 men (24.1%) and 192 women (75.9%). The adults ranged in age from 18 to 87 years, with 76 younger adults (M age = 23.89, range 18– 29 years), 96 early middle-aged adults (M age = 38.91, range 30–49) and 81 late middle-age and older adults (M age = 58.73, range 50–87). 2.3. Measures 2.3.1. Number of goals Drawn from the broader research on behavioral change and positive psychology, adults were queried regarding nine health behavioral
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change goals. Participants endorsed a mean of 3.31 (SD = 1.93) of the nine goals, including to: lose weight (59.3%), gain weight (3.2%), increase exercise (65.6%), reduce tobacco use (9.1%), reduce alcohol use (14.6%), walk more (62.1%), eat healthier foods (71.5%), sleep more (40.3%), and sleep less (5.5%). Although count data are often not appropriate for use as outcomes in regression models, the current measure was normally distributed, with skew and kurtosis in acceptable ranges. Moreover, there were few scores of zero (11%). 2.3.2. Time perspective The Time Attitudes Scale (TAS; Mello & Worrell, 2012; Mello et al., 2016; Worrell, Mello, & Buhl, 2011) was used to index temporal perspectives. The TAS included 30 items, each scored on a 5-point Likerttype scale. Six subscales are rendered, with higher scores representing higher levels of the underlying construct. The six subscales are: positive past (e.g., I have very happy memories of my childhood), negative past (e.g., My past is a time in my life that I would like to forget), positive present (e.g., I am happy with my current life), negative present (e.g., I am not satisfied with my life right now), positive future (e.g., I look forward to my future), and negative future (e.g., I doubt I will make something of myself). 3. Results 3.1. Preliminary analyses Completion rates for individual time perspective items were high, with fewer than 1% of the items missing. Individual item mean imputation for 13 missing data points was used. Indices for the six valenced temporal frames were computed, and were similar to those reported by Mello et al. (2016), for: positive past (α = 0.91); negative past (α = 0.91); positive present (α = 0.94); negative present (α = 0.89); positive future (α = 0.93); and negative future (α = 0.84). Because women were over-represented in our sample, we examined the relation of gender with the model constructs using partial correlations and t-tests. In no instance did gender relate significantly with temporal frame, valence, or health goals. Thus, for the sake of parsimony, we did not include gender in our model testing. Based on inspection of the first four moments of the distributions, the data met the underlying assumptions of the General Linear Model (GLM). As shown in Table 1, younger adults had a significantly lower positive past score than early middle-aged adults. No other age group differences were observed in the constructs of interest. Similarly, based on the correlation coefficients shown in Table 2, no significant associations were observed between age and the number of health goals (r(253) = 0.10, p = 0.11). Correlations between age and the valenced temporal frames were small, and with the exception of negative past and age (r(253) = − 0.15, p = 0.02), they failed to reach significance. Despite these low bivariate associations, age was retained in the model because whether age exerted indirect effects on health change goals via the different valenced temporal frames was of interest. 3.2. Model testing The well-known Baron and Kenny (1986) approach to mediation requires a significant regression path between the predictor variable (X) and the outcome (Y). If this relation is not evident, the analysis stops, precluding an examination of indirect effects of the predictor on the outcome through its association with the proposed mediator. An additional limitation to this approach lies in the inflated statistical error due to the use of multiple tests (Fritz & MacKinnon, 2007). An alternative approach is to examine conditional and indirect effects, whereby the predictor which fails to exert direct effects on an outcome might exert influence via its association with the mediator. In order to examine both direct effects of age on health goals and the indirect effects of
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Table 1 One-way analysis of variance tests of mean age differences.
Age Positive past Negative past Positive present Negative present Positive future Negative future Number of health goals
Younger adults (n = 76)
Early midlife adults (n = 96)
Late midlife and older adults (n = 81)
F (2, 250)
23.89 (3.11) 16.67 (4.55) 13.52 (5.46) 17.67 (5.20) 13.45 (5.17) 19.90 (4.44) 9.89 (4.13) 3.25 (1.92)
38.91 (6.16) 18.58 (3.97) 12.45 (4.55) 17.92 (4.24) 13.68 (4.81) 19.52 (3.68) 9.43 (3.50) 3.20 (2.02)
58.73 (6.05) 17.62 (4.51) 11.97 (5.27) 18.16 (4.86) 13.00 (4.94) 19.38 (4.57) 9.84 (4.07) 3.51 (1.84)
828.91⁎⁎ 4.12⁎ 1.91 0.21 0.42 0.32 0.39 0.62
YA b EMA
⁎ p b 0.05. ⁎⁎ p b 0.001.
age on health goals via the association among age and temporal perspectives, we used the PROCESS macro (Hayes, 2012). The model depicted in Fig. 1 was tested using a single mediation regression analysis, implemented in the PROCESS macro (Hayes, 2012); significant paths are solid lines. In addition to testing the overall model, each path was assessed for significance. Results of individual analyses are presented in Table 3. The seven predictors accounted for a small but significant portion of variance in the number of adults' health goals, F (7, 245) = 4.82, p b 0.001, R2 = 0.121. Only positive present exerted direct effects on the number of health goals (b = 0.140, SE(b) = 0.054). Age exerted a small and non-significant indirect effect on the number of health goals via its association with positive present (b = 0.012, SE = 0.008). In addition, age exhibited a significant association with negative past, F (1, 251) = 5.47, p b 0.05, R2 = 0.021 (b = − 0.050, SE = 0.021), but neither age (b = 0.012, SE = 0.008) nor negative past (b = − 0.008, SE = 0.040) related significantly to health goals.
age group, the results speak to the age-invariant process of how temporal perspectives relate to health goals. Each of these contributions is discussed in more depth in the sections that follow. 4.1. Temporal perspectives: frame and valence Although there is a large literature examining various temporal frames, most studies focus on a single temporal frame (Boniwell & Zimbardo, 2004). This is especially true in the literature among college-aged adults (Henson et al., 2006) and older adults (e.g., Brothers et al., 2014; Carstensen & Fredrickson, 1998). Like Mello et al. (2016), we have observed significant correlations among the six valenced temporal frames. In our data, associations within temporal frame were highly correlated and associations across valence were moderately correlated. That is, a general positivity about the past, present and future was evident. Although Mello et al.'s (2016) work suggests that temporal perspectives are distinct from indices of subjective wellbeing, there is still considerable overlap, especially when the present temporal frame is considered.
4. Discussion Despite the intuitive appeal of the idea that temporal frames might differentially influence developmental outcomes (Lewin, 1939; Nuttin & Lens, 1985), relatively few investigations have included multiple temporal frames simultaneously (Boniwell & Zimbardo, 2004; Stolarski, Wiberg, & Osin, 2015). Health goals vary from person to person and may be affected by the individual characteristics of age, valenced temporal perspective, intellectual curiosity, and motivation. With the development of a valid measure of valenced temporal perspectives (Mello et al., 2016), the field can begin to address whether and in what ways temporal perspectives relate to important adult outcomes across the lifespan. Thus, we analyzed the associations among age, six valenced temporal frames, and health goals among community-dwelling adults. Our study contributes to the literature on temporal perspectives in three ways. Firstly, by extending the focus beyond future perspective to include the contributions of past and present perspectives. Secondly, rather than focusing on a single behavior (e.g., smoking cessation, physical activity), the investigation to the superordinate construct of health goals was extended. Finally, by extending our sample beyond a single
4.2. Health goals and time Although there is a literature demonstrating age differences in the number and content of goals held by adults (e.g., Brothers et al., 2014; Carstensen & Fredrickson, 1998), we know of no other research that has explicitly examined the relations between age, temporal perspective and health-related goals. Although the majority of adults in Rossi and Isaacowitz's (2006) sample endorsed health-related goals when presented with a checklist format, we did not see such strong effects. Approximately 40–50% of our sample endorsed increasing health-promotion behaviors, such as exercising more often. A smaller percentage endorsed limiting health risk behaviors, such as alcohol intake and tobacco use. On average, adults in our sample were interested in about three of the nine health change goals. At the group level, no significant age group differences emerged in the number of health change goals. Age group differences were detected for only one of the six valenced temporal frames: early midlife adults reported higher positive past scores than did younger adults. This may be
Table 2 Pearson correlations of age, time perspectives, and goals (N = 253).
1. Age 2. Health goals 3. Positive past 4. Negative past 5. Positive present 6. Negative present 7. Positive future 8. Negative future ⁎ p b 0.05. ⁎⁎ p b 0.01.
M
SD
1
40.74 3.31 17.70 12.62 17.92 13.39 19.59 9.70
14.82 1.93 4.38 5.09 4.73 4.95 4.20 3.87
1.0 0.100 −0.073 −0.146⁎ 0.079 −0.086 −0.032 −0.023
2
3
4
5
6
7
8
1.0 0.193⁎⁎ −0.175⁎⁎ 0.293⁎⁎ −0.207⁎⁎ 0.275⁎⁎ −0.253⁎⁎
1.0 −0.733⁎⁎ 0.368⁎⁎ −0.309⁎⁎ 0.360⁎⁎ −0.423⁎⁎
1.0 −0.376⁎⁎ 0.374⁎⁎ −0.326⁎⁎ 0.451⁎⁎
1.0 −0.869⁎⁎ 0.687⁎⁎ −0.640⁎⁎
1.0 −0.640⁎⁎ 0.609⁎⁎
1.0 −0.772⁎⁎
1.0
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differences in health goals are minimized. Thus, among adults who seek to make behavior changes, age may be less important. Thus, it is likely that our failure to detect age effects is due to a true lack of such effects. That is, in using a broader measure of temporal frames and using them simultaneously, we may have a more accurate view of temporal perspective in adulthood. That view suggests that although there may be fluctuations in temporal perspective across the life span (e.g., Nelson, Willoughby, Rogers, & Padilla-Walker, 2015), its relation with outcomes may be age-invariant. 4.3. Limitations and future directions
Fig. 1. Tested model of direct and indirect effects of age on health goals through temporal perspective. Note. Significant pathways are represented by solid lines.
an idiosyncratic effect in our sample or it may be a result that warrants further investigation. It may be that our early midlife adults, who were roughly age 40 years, were just beginning to experience a turning point as they moved more firmly into midlife. Based on other work (Stahl & Patrick, 2012), this period may be a time in which temporal perspectives and health goals become especially salient. Although we filed to detect an influence of gender on the model, it is possible that a less-targeted recruitment or a larger sample would be more sensitive to gender differences. It is also possible, that as with age, few gender differences in health goals exist. Additional research is needed in order to more carefully examine gender effects and its interaction with age on health goals and health behaviors. In the examination of the direct and indirect effects of age on health goals, age did not exert a significant effect on health goals. Our finding is contrary to much of the published literature; we think that our result is partly related to our sample. First, we think that the lack of age effects may be related to our sampling strategy. Despite its modest size, our sample includes adults from across the life span, whereas other studies often rely on a single age group, in which age is held constant, or rely on an extreme groups design, that may magnify age differences. A second sampling issue relates to the way in which we advertised the study to the community. Based on our title, it was clear that we were targeting adults with interests in health behaviors and behavioral change. It is likely that when such targeted recruitment efforts are deployed, age
Table 3 Mediated regression analysis (N = 253).
Age ➔ Health goals Age ➔ Positive past Age ➔ Negative past Age ➔ Positive present Age ➔ Negative present Age ➔ Positive future Age ➔ Negative future Positive past ➔ Health goals Negative past ➔ Health goals Positive present ➔ Health goals Negative present ➔ Health goals Positive future ➔ Health goals Negative future ➔ Health goals
b
SE
t
0.012 0.022 −0.050⁎ 0.025 −0.029 −0.009 −0.006 0.023 −0.008 0.140⁎ 0.089 −0.009 −0.006
0.008 0.019 0.021 0.020 0.021 0.018 0.017 0.040 0.035 0.054 0.048 0.018 0.017
1.45 1.16 −2.34 1.26 −1.36 −0.51 −0.36 0.58 −0.22 2.59 1.85 −0.51 −0.36
CR N 1.96 are significant at p b 0.05; CR N 2.16 are significant at p b 0.01. ⁎ p b 0.05.
Although our sample is relatively small and did not include a focus on adults over age 75 years, our study contributes to the understanding of the ways in which valenced temporal perspectives relate to behavioral change goals across adulthood. There are other limitations, to our study, however. Much of the current research on individual differences shows that once the effects of cognitive ability have been partialled out, the strength of the initial predictor seems to be diminished. To that end, future research assessing valenced temporal perspective may want to assess intelligence to see if temporal effects still exist in association with health goals. Nuttin and Lens (1985) and others (e.g., Kazakina, 2015) have highlighted that time orientation is a multidimensional construct, comprised of temporal frame, attitudinal valence, extension, structuration and vividness/realism. Although we included six valenced temporal frames, other aspects of time orientation should be considered in future research. To wit, we did not include an examination of the length or depth of temporal extension. Future examinations might examine how a focus on the distant past or distant future differentially influences current and near-future goal pursuit. There are some hints in the time orientation literature that suggest that the ways in which younger, middle-aged and older adults define temporal frames differs (Fingerman & Perlmutter, 2001; Lang & Carstensen, 2002). Such aspects likely interact with goal pursuit, as evidenced by recent findings from Kornadt and Rothermund (2014). The issues of structuration or degree of connectedness within and across temporal frames, and the degree of vividness and realism of temporal perspectives are especially intriguing and warrant additional examinations across the life span. Although some researchers have discussed these aspects of time orientation (Boniwell & Zimbardo, 2004; Stolarski et al., 2015), little research has explicitly examined these. Our analyses focused on a relatively small sample at a single point in time. Thus, we were unable to examine the potential interplay of temporal perspectives across the context of a person's own developmental history. Longitudinal studies could examine whether and in what ways perceptions of the present become valenced perceptions of the past, and whether such perspectives influence future perspectives and behaviors. This linked cycle may directly lead one to anticipate negative future events and may decrease a person's likelihood to pursue or succeed in specific behavioral change goals (Stolarski et al., 2014). Acknowledgements We gratefully acknowledge the assistance of Dr. Nipat Brock Pichayayothin, who suggested to use the Mello et al. measure. We also acknowledge the financial support of the Institute on Coaching at McLean Hospital/Harvard. References Baltes, P. B., & Baltes, M. (1990). Psychological perspectives on successful aging: The model of selective optimization and compensation. In P. Baltes, & M. Baltes (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 1–34). New York, NY, US: Cambridge University Press. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of
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Contents lists available at ScienceDirect
Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Time for a change: Temporal perspectives and health goals Amy Knepple Carney ⁎, Julie Hicks Patrick Department of Psychology, West Virginia University, United States
a r t i c l e
i n f o
Article history: Received 19 September 2016 Received in revised form 23 December 2016 Accepted 9 January 2017 Available online xxxx Keywords: Temporal perspectives Health goals
a b s t r a c t Future time perspective influences the relations among age and health behaviors (Stahl & Patrick, 2012). However, past and present temporal perspectives may also play a role in the goals that people pursue. Whether age interacts with temporal perspectives to influence health goals has not been fully examined. Thus, the aim of the current study was to examine the associations among age, different temporal frames, and behavioral change goals related to health. We examined these questions using data provided by 253 adults, ages 18–87 years (M = 40.74, SD = 14.82). Results from mediational regression analyses showed that age and a full range of valenced temporal perspectives accounted for 12% of the variance in the number of health goals adults endorsed. Only positive present temporal perspective related directly to health goals. By using a broader measure of temporal frames simultaneously, we may have a more accurate view of temporal perspective in adulthood. © 2017 Elsevier Ltd. All rights reserved.
1. Introduction Temporal perspectives involve attitudes, thoughts, and affective tone regarding our personal past, present, and future. As such, temporal perspectives influence our current and future behaviors (Lennings, 2000). Although a multidimensional construct comprised of temporal frames (i.e., past, present, future), depth or length of extension (e.g., near past, far future), emotional valence (e.g., positive, neutral, negative), structuration or degree of continuity across temporal frames, and the vividness and realism of one's views (Kazakina, 2015; Nuttin & Lens, 1985), most researchers have considered only one or two of the dimensions of temporal perspectives, with the bulk of research focusing on future time perspectives (Boniwell & Zimbardo, 2004). The perception of future time as either expansive or limited influences most behaviors and psychological processes in adulthood (Carstensen, 2006). Goal selection is inherently a future-focused behavior that is influenced by our temporal perspective (Baltes & Baltes, 1990; Deci & Ryan, 2000). Relative to a narrow future horizon, individuals with an expansive future time perspective generally have more goals (Brothers, Chui, & Diehl, 2014), more motivation to work on goals (Lens, Paixao, Herrera, & Grobler, 2012), anticipate having energy to complete tasks (Stolarski, Matthews, Postek, Zimbardo, & Bitner, 2014), and report more frequent engagement in health-promoting behaviors (Gellert, Ziegelmann, Lippke, & Schwarzer, 2012; Henson, Carey, Carey, & Maisto, 2006; Stahl & Patrick, 2012). Although there is some literature to suggest that future goals are directly shaped by our present feelings and behaviors (Lewin, 1939; Nuttin & Lens, 1985), ⁎ Corresponding author. E-mail address: [email protected] (A. Knepple Carney).
http://dx.doi.org/10.1016/j.paid.2017.01.015 0191-8869/© 2017 Elsevier Ltd. All rights reserved.
whether past and the present influence goal selection remains relatively unexamined.
1.1. Time and health-related goals Much of the current literature on the role of temporal perspective and health behaviors focuses on discrete health behaviors among college students or among community-dwelling younger and middleaged adults. Kornadt and Rothermund (2014) discussed the implications of focusing on domain-general versus domain-specific behaviors, including the need to isolate underlying motivations for behavior change within domains. Of course, extending investigations beyond midlife is also an important endeavor, as differences in goals and in the motivations for pursuing those goals may vary across age, temporal perspective, or both (Rossi & Isaacowitz, 2006). Using data from a large group of undergraduates (N = 1568), Henson et al. (2006) found that students with an expansive future perspective were less likely to engage in high-risk health behaviors, such as substance abuse. Expansive future time perspective was also related to health-promotion behaviors, including more exercise, more frequent condom use, and among women, use of birth control. Similar results are found in non-college samples of adults, as well. Across several different nations and age groups, an expansive future time perspective predicts successful smoking cessation 8 years later (Hall, Fong, & Meng, 2014). Future orientation has been linked to health promotion behaviors in community-dwelling adults, as well. Among younger and middle-aged adults, Stahl and Patrick (2012) reported that age effects on physical activity were partially mediated by future perspective. Gellert et al. (2012) also reported associations with future
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perspective and physical activity goals to predict physical activity 6 months after baseline. Although there is evidence that future perspective influences both risky health behaviors and health promotion behaviors, few studies have simultaneously examined other temporal frames. An exception is a recent study reported by Griva, Tseferidi, and Anagnostopoulos (2015). Using data from more than 400 adults (ages 18–56 years), they reported a link between future orientation and physical exercise, but they also found roles for past and present orientations as related to body mass index (BMI). Thus, a growing literature supports the relations among various temporal frames and specific health behaviors, in isolation. Whether and in what ways past, present, and future frames influence risky health behaviors and health promotion behaviors among a wide age range of adults have not been examined. 1.2. Current study The current study focuses on goals to improve physical health because they are relatively under-studied in the area of temporal perspective and because they have important implications for communitybased health promotion efforts (Fisher et al., 2011). Based on the work by Rossi and Isaacowitz (2006) and Kornadt and Rothermund (2014), we anticipated that younger adults would have fewer health change goals relative to their more senior counterparts. However, there is little evidence to suggest whether age differences would be evident in the type of health goals endorsed. We also anticipated significant associations between age and temporal perspectives (Carstensen, Isaacowitz, & Charles, 1999; Spreng & Levine, 2006). The main focus, however, was to examine whether temporal frames governed the association between the number health goals across age groups. Although Fingerman and Perlmutter (2001) suggested that time might operate differently across age groups, research examining the effects of multiple temporal perspectives is rare. 2. Method 2.1. Power analysis A formal power analysis, implemented in G*Power (Erdfeld, Faul, & Buchner, 1996), suggested that data from 253 adults would provide sufficient power (power N 0.95) to detect medium-sized effects (f 2 = 0.15) in a 7-variable regression equation (p b 0.05). 2.2. Participants Participants were drawn from community-dwelling adults who responded to print and electronic ads to complete an online prescreen for a health coaching study. The prescreen consisted of a 25-page online survey. Participants completed the survey in about 38 min (SD = 31.4, range 25–60 min). Although the prescreen for the larger study included a variety of measures (e.g., physical and mental health, decision making, technology use, and social exchanges), only those related to the current study are discussed in this report. All participants were offered $5 for completing the survey. A total of 285 adults started the instrument, with 253 completing the temporal perspective and demographic items. Respondents consisted of 61 men (24.1%) and 192 women (75.9%). The adults ranged in age from 18 to 87 years, with 76 younger adults (M age = 23.89, range 18– 29 years), 96 early middle-aged adults (M age = 38.91, range 30–49) and 81 late middle-age and older adults (M age = 58.73, range 50–87). 2.3. Measures 2.3.1. Number of goals Drawn from the broader research on behavioral change and positive psychology, adults were queried regarding nine health behavioral
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change goals. Participants endorsed a mean of 3.31 (SD = 1.93) of the nine goals, including to: lose weight (59.3%), gain weight (3.2%), increase exercise (65.6%), reduce tobacco use (9.1%), reduce alcohol use (14.6%), walk more (62.1%), eat healthier foods (71.5%), sleep more (40.3%), and sleep less (5.5%). Although count data are often not appropriate for use as outcomes in regression models, the current measure was normally distributed, with skew and kurtosis in acceptable ranges. Moreover, there were few scores of zero (11%). 2.3.2. Time perspective The Time Attitudes Scale (TAS; Mello & Worrell, 2012; Mello et al., 2016; Worrell, Mello, & Buhl, 2011) was used to index temporal perspectives. The TAS included 30 items, each scored on a 5-point Likerttype scale. Six subscales are rendered, with higher scores representing higher levels of the underlying construct. The six subscales are: positive past (e.g., I have very happy memories of my childhood), negative past (e.g., My past is a time in my life that I would like to forget), positive present (e.g., I am happy with my current life), negative present (e.g., I am not satisfied with my life right now), positive future (e.g., I look forward to my future), and negative future (e.g., I doubt I will make something of myself). 3. Results 3.1. Preliminary analyses Completion rates for individual time perspective items were high, with fewer than 1% of the items missing. Individual item mean imputation for 13 missing data points was used. Indices for the six valenced temporal frames were computed, and were similar to those reported by Mello et al. (2016), for: positive past (α = 0.91); negative past (α = 0.91); positive present (α = 0.94); negative present (α = 0.89); positive future (α = 0.93); and negative future (α = 0.84). Because women were over-represented in our sample, we examined the relation of gender with the model constructs using partial correlations and t-tests. In no instance did gender relate significantly with temporal frame, valence, or health goals. Thus, for the sake of parsimony, we did not include gender in our model testing. Based on inspection of the first four moments of the distributions, the data met the underlying assumptions of the General Linear Model (GLM). As shown in Table 1, younger adults had a significantly lower positive past score than early middle-aged adults. No other age group differences were observed in the constructs of interest. Similarly, based on the correlation coefficients shown in Table 2, no significant associations were observed between age and the number of health goals (r(253) = 0.10, p = 0.11). Correlations between age and the valenced temporal frames were small, and with the exception of negative past and age (r(253) = − 0.15, p = 0.02), they failed to reach significance. Despite these low bivariate associations, age was retained in the model because whether age exerted indirect effects on health change goals via the different valenced temporal frames was of interest. 3.2. Model testing The well-known Baron and Kenny (1986) approach to mediation requires a significant regression path between the predictor variable (X) and the outcome (Y). If this relation is not evident, the analysis stops, precluding an examination of indirect effects of the predictor on the outcome through its association with the proposed mediator. An additional limitation to this approach lies in the inflated statistical error due to the use of multiple tests (Fritz & MacKinnon, 2007). An alternative approach is to examine conditional and indirect effects, whereby the predictor which fails to exert direct effects on an outcome might exert influence via its association with the mediator. In order to examine both direct effects of age on health goals and the indirect effects of
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Table 1 One-way analysis of variance tests of mean age differences.
Age Positive past Negative past Positive present Negative present Positive future Negative future Number of health goals
Younger adults (n = 76)
Early midlife adults (n = 96)
Late midlife and older adults (n = 81)
F (2, 250)
23.89 (3.11) 16.67 (4.55) 13.52 (5.46) 17.67 (5.20) 13.45 (5.17) 19.90 (4.44) 9.89 (4.13) 3.25 (1.92)
38.91 (6.16) 18.58 (3.97) 12.45 (4.55) 17.92 (4.24) 13.68 (4.81) 19.52 (3.68) 9.43 (3.50) 3.20 (2.02)
58.73 (6.05) 17.62 (4.51) 11.97 (5.27) 18.16 (4.86) 13.00 (4.94) 19.38 (4.57) 9.84 (4.07) 3.51 (1.84)
828.91⁎⁎ 4.12⁎ 1.91 0.21 0.42 0.32 0.39 0.62
YA b EMA
⁎ p b 0.05. ⁎⁎ p b 0.001.
age on health goals via the association among age and temporal perspectives, we used the PROCESS macro (Hayes, 2012). The model depicted in Fig. 1 was tested using a single mediation regression analysis, implemented in the PROCESS macro (Hayes, 2012); significant paths are solid lines. In addition to testing the overall model, each path was assessed for significance. Results of individual analyses are presented in Table 3. The seven predictors accounted for a small but significant portion of variance in the number of adults' health goals, F (7, 245) = 4.82, p b 0.001, R2 = 0.121. Only positive present exerted direct effects on the number of health goals (b = 0.140, SE(b) = 0.054). Age exerted a small and non-significant indirect effect on the number of health goals via its association with positive present (b = 0.012, SE = 0.008). In addition, age exhibited a significant association with negative past, F (1, 251) = 5.47, p b 0.05, R2 = 0.021 (b = − 0.050, SE = 0.021), but neither age (b = 0.012, SE = 0.008) nor negative past (b = − 0.008, SE = 0.040) related significantly to health goals.
age group, the results speak to the age-invariant process of how temporal perspectives relate to health goals. Each of these contributions is discussed in more depth in the sections that follow. 4.1. Temporal perspectives: frame and valence Although there is a large literature examining various temporal frames, most studies focus on a single temporal frame (Boniwell & Zimbardo, 2004). This is especially true in the literature among college-aged adults (Henson et al., 2006) and older adults (e.g., Brothers et al., 2014; Carstensen & Fredrickson, 1998). Like Mello et al. (2016), we have observed significant correlations among the six valenced temporal frames. In our data, associations within temporal frame were highly correlated and associations across valence were moderately correlated. That is, a general positivity about the past, present and future was evident. Although Mello et al.'s (2016) work suggests that temporal perspectives are distinct from indices of subjective wellbeing, there is still considerable overlap, especially when the present temporal frame is considered.
4. Discussion Despite the intuitive appeal of the idea that temporal frames might differentially influence developmental outcomes (Lewin, 1939; Nuttin & Lens, 1985), relatively few investigations have included multiple temporal frames simultaneously (Boniwell & Zimbardo, 2004; Stolarski, Wiberg, & Osin, 2015). Health goals vary from person to person and may be affected by the individual characteristics of age, valenced temporal perspective, intellectual curiosity, and motivation. With the development of a valid measure of valenced temporal perspectives (Mello et al., 2016), the field can begin to address whether and in what ways temporal perspectives relate to important adult outcomes across the lifespan. Thus, we analyzed the associations among age, six valenced temporal frames, and health goals among community-dwelling adults. Our study contributes to the literature on temporal perspectives in three ways. Firstly, by extending the focus beyond future perspective to include the contributions of past and present perspectives. Secondly, rather than focusing on a single behavior (e.g., smoking cessation, physical activity), the investigation to the superordinate construct of health goals was extended. Finally, by extending our sample beyond a single
4.2. Health goals and time Although there is a literature demonstrating age differences in the number and content of goals held by adults (e.g., Brothers et al., 2014; Carstensen & Fredrickson, 1998), we know of no other research that has explicitly examined the relations between age, temporal perspective and health-related goals. Although the majority of adults in Rossi and Isaacowitz's (2006) sample endorsed health-related goals when presented with a checklist format, we did not see such strong effects. Approximately 40–50% of our sample endorsed increasing health-promotion behaviors, such as exercising more often. A smaller percentage endorsed limiting health risk behaviors, such as alcohol intake and tobacco use. On average, adults in our sample were interested in about three of the nine health change goals. At the group level, no significant age group differences emerged in the number of health change goals. Age group differences were detected for only one of the six valenced temporal frames: early midlife adults reported higher positive past scores than did younger adults. This may be
Table 2 Pearson correlations of age, time perspectives, and goals (N = 253).
1. Age 2. Health goals 3. Positive past 4. Negative past 5. Positive present 6. Negative present 7. Positive future 8. Negative future ⁎ p b 0.05. ⁎⁎ p b 0.01.
M
SD
1
40.74 3.31 17.70 12.62 17.92 13.39 19.59 9.70
14.82 1.93 4.38 5.09 4.73 4.95 4.20 3.87
1.0 0.100 −0.073 −0.146⁎ 0.079 −0.086 −0.032 −0.023
2
3
4
5
6
7
8
1.0 0.193⁎⁎ −0.175⁎⁎ 0.293⁎⁎ −0.207⁎⁎ 0.275⁎⁎ −0.253⁎⁎
1.0 −0.733⁎⁎ 0.368⁎⁎ −0.309⁎⁎ 0.360⁎⁎ −0.423⁎⁎
1.0 −0.376⁎⁎ 0.374⁎⁎ −0.326⁎⁎ 0.451⁎⁎
1.0 −0.869⁎⁎ 0.687⁎⁎ −0.640⁎⁎
1.0 −0.640⁎⁎ 0.609⁎⁎
1.0 −0.772⁎⁎
1.0
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differences in health goals are minimized. Thus, among adults who seek to make behavior changes, age may be less important. Thus, it is likely that our failure to detect age effects is due to a true lack of such effects. That is, in using a broader measure of temporal frames and using them simultaneously, we may have a more accurate view of temporal perspective in adulthood. That view suggests that although there may be fluctuations in temporal perspective across the life span (e.g., Nelson, Willoughby, Rogers, & Padilla-Walker, 2015), its relation with outcomes may be age-invariant. 4.3. Limitations and future directions
Fig. 1. Tested model of direct and indirect effects of age on health goals through temporal perspective. Note. Significant pathways are represented by solid lines.
an idiosyncratic effect in our sample or it may be a result that warrants further investigation. It may be that our early midlife adults, who were roughly age 40 years, were just beginning to experience a turning point as they moved more firmly into midlife. Based on other work (Stahl & Patrick, 2012), this period may be a time in which temporal perspectives and health goals become especially salient. Although we filed to detect an influence of gender on the model, it is possible that a less-targeted recruitment or a larger sample would be more sensitive to gender differences. It is also possible, that as with age, few gender differences in health goals exist. Additional research is needed in order to more carefully examine gender effects and its interaction with age on health goals and health behaviors. In the examination of the direct and indirect effects of age on health goals, age did not exert a significant effect on health goals. Our finding is contrary to much of the published literature; we think that our result is partly related to our sample. First, we think that the lack of age effects may be related to our sampling strategy. Despite its modest size, our sample includes adults from across the life span, whereas other studies often rely on a single age group, in which age is held constant, or rely on an extreme groups design, that may magnify age differences. A second sampling issue relates to the way in which we advertised the study to the community. Based on our title, it was clear that we were targeting adults with interests in health behaviors and behavioral change. It is likely that when such targeted recruitment efforts are deployed, age
Table 3 Mediated regression analysis (N = 253).
Age ➔ Health goals Age ➔ Positive past Age ➔ Negative past Age ➔ Positive present Age ➔ Negative present Age ➔ Positive future Age ➔ Negative future Positive past ➔ Health goals Negative past ➔ Health goals Positive present ➔ Health goals Negative present ➔ Health goals Positive future ➔ Health goals Negative future ➔ Health goals
b
SE
t
0.012 0.022 −0.050⁎ 0.025 −0.029 −0.009 −0.006 0.023 −0.008 0.140⁎ 0.089 −0.009 −0.006
0.008 0.019 0.021 0.020 0.021 0.018 0.017 0.040 0.035 0.054 0.048 0.018 0.017
1.45 1.16 −2.34 1.26 −1.36 −0.51 −0.36 0.58 −0.22 2.59 1.85 −0.51 −0.36
CR N 1.96 are significant at p b 0.05; CR N 2.16 are significant at p b 0.01. ⁎ p b 0.05.
Although our sample is relatively small and did not include a focus on adults over age 75 years, our study contributes to the understanding of the ways in which valenced temporal perspectives relate to behavioral change goals across adulthood. There are other limitations, to our study, however. Much of the current research on individual differences shows that once the effects of cognitive ability have been partialled out, the strength of the initial predictor seems to be diminished. To that end, future research assessing valenced temporal perspective may want to assess intelligence to see if temporal effects still exist in association with health goals. Nuttin and Lens (1985) and others (e.g., Kazakina, 2015) have highlighted that time orientation is a multidimensional construct, comprised of temporal frame, attitudinal valence, extension, structuration and vividness/realism. Although we included six valenced temporal frames, other aspects of time orientation should be considered in future research. To wit, we did not include an examination of the length or depth of temporal extension. Future examinations might examine how a focus on the distant past or distant future differentially influences current and near-future goal pursuit. There are some hints in the time orientation literature that suggest that the ways in which younger, middle-aged and older adults define temporal frames differs (Fingerman & Perlmutter, 2001; Lang & Carstensen, 2002). Such aspects likely interact with goal pursuit, as evidenced by recent findings from Kornadt and Rothermund (2014). The issues of structuration or degree of connectedness within and across temporal frames, and the degree of vividness and realism of temporal perspectives are especially intriguing and warrant additional examinations across the life span. Although some researchers have discussed these aspects of time orientation (Boniwell & Zimbardo, 2004; Stolarski et al., 2015), little research has explicitly examined these. Our analyses focused on a relatively small sample at a single point in time. Thus, we were unable to examine the potential interplay of temporal perspectives across the context of a person's own developmental history. Longitudinal studies could examine whether and in what ways perceptions of the present become valenced perceptions of the past, and whether such perspectives influence future perspectives and behaviors. This linked cycle may directly lead one to anticipate negative future events and may decrease a person's likelihood to pursue or succeed in specific behavioral change goals (Stolarski et al., 2014). Acknowledgements We gratefully acknowledge the assistance of Dr. Nipat Brock Pichayayothin, who suggested to use the Mello et al. measure. We also acknowledge the financial support of the Institute on Coaching at McLean Hospital/Harvard. References Baltes, P. B., & Baltes, M. (1990). Psychological perspectives on successful aging: The model of selective optimization and compensation. In P. Baltes, & M. Baltes (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 1–34). New York, NY, US: Cambridge University Press. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of
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